Another trend I believe was a key contributing factor is big tech's adoption and widespread distribution of LLMs. Think about a scenario where OpenAI doesn't launch ChatGPT and Microsoft keeps their Bing chatbot under wraps. In that setting, many developers might feel they have to fix the "hallucination" issue before releasing anything.
Sure, Microsoft, OpenAI, and later Google faced criticism for users' early encounters with LLMs going off-track [0], but thanks to that, and only a year after ChatGPT's launch, even the average user is aware that hallucinations can occur in AI responses. This widespread understanding helps LLM builders and incumbents deploy LLM-powered apps faster and with less scrutiny.
Thank you for this summary. For me, the crucial part is, "But as it turns out, efficiently learning the relationships between pieces of data is useful for many, many domains beyond translation." As the dust settles, for me, it becomes increasingly evident that the quality of data and the ability to accurately establish these relationships are paramount for the next phase.
In addition to the importance of data quality, it is worth delving into the significance of semantic mapping in this context. Semantic mapping plays a pivotal role in enabling AI systems to not only understand data relationships but also to derive meaningful insights and context from diverse datasets.
Yours is a great explanation of the technological advancements that made it possible to develop increasingly impressive models across the spectrum. That's the "supply" side of things.
But I still feel like ChatGPT was the true inflection point in terms of helping AI make the jump into mass consciousness. The demand side.
Prior to that, I personally was already experimenting with GPT-3 all the way back in 2021 and finding its ability to write fiction, poems, etc. on demand to be really impressive. (Even though it lagged well behind GPT-4.) But when I showed it to friends and family, I still got a "nice parlour trick, but what's the point" level of reaction. ChatGPT changed all that and truly kicked off the current hype cycle.
So we're now at a point where both the "supply" and "demand" are on the rise at just the right time: The "demand" / hype side makes it lucrative and worthwhile for startups and incumbents to want to develop AI products further to begin with, while the "supply" side makes it possible for ever-smaller companies to create models and products of their own.
Like you, I don't know where we are on the S-curve, but one thing's certain: It's not a boring time to be alive.
Another trend I believe was a key contributing factor is big tech's adoption and widespread distribution of LLMs. Think about a scenario where OpenAI doesn't launch ChatGPT and Microsoft keeps their Bing chatbot under wraps. In that setting, many developers might feel they have to fix the "hallucination" issue before releasing anything.
Sure, Microsoft, OpenAI, and later Google faced criticism for users' early encounters with LLMs going off-track [0], but thanks to that, and only a year after ChatGPT's launch, even the average user is aware that hallucinations can occur in AI responses. This widespread understanding helps LLM builders and incumbents deploy LLM-powered apps faster and with less scrutiny.
[0] https://fortune.com/2023/02/21/bing-microsoft-sydney-chatgpt-openai-controversy-toxic-a-i-risk + https://www.npr.org/2023/02/09/1155650909/google-chatbot--error-bard-shares
Thank you for this summary. For me, the crucial part is, "But as it turns out, efficiently learning the relationships between pieces of data is useful for many, many domains beyond translation." As the dust settles, for me, it becomes increasingly evident that the quality of data and the ability to accurately establish these relationships are paramount for the next phase.
In addition to the importance of data quality, it is worth delving into the significance of semantic mapping in this context. Semantic mapping plays a pivotal role in enabling AI systems to not only understand data relationships but also to derive meaningful insights and context from diverse datasets.
This is a solid and thorough take!
Yours is a great explanation of the technological advancements that made it possible to develop increasingly impressive models across the spectrum. That's the "supply" side of things.
But I still feel like ChatGPT was the true inflection point in terms of helping AI make the jump into mass consciousness. The demand side.
Prior to that, I personally was already experimenting with GPT-3 all the way back in 2021 and finding its ability to write fiction, poems, etc. on demand to be really impressive. (Even though it lagged well behind GPT-4.) But when I showed it to friends and family, I still got a "nice parlour trick, but what's the point" level of reaction. ChatGPT changed all that and truly kicked off the current hype cycle.
So we're now at a point where both the "supply" and "demand" are on the rise at just the right time: The "demand" / hype side makes it lucrative and worthwhile for startups and incumbents to want to develop AI products further to begin with, while the "supply" side makes it possible for ever-smaller companies to create models and products of their own.
Like you, I don't know where we are on the S-curve, but one thing's certain: It's not a boring time to be alive.